Category Archives: Artificial Intelligence
This online program from the MIT Sloan School of Management and the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) challenges common misconceptions surrounding AI and will equip and encourage you to embrace AI as part of a transformative toolkit. With a focus on the organizational and managerial implications of these technologies, rather than on their technical aspects, youll leave this course armed with the knowledge and confidence you need to pioneer its successful integration in business.
What is artificial intelligence (AI)? What does it mean for business? And how can your company take advantage of it? This online program, designed by the MIT Sloan School of Management and the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), will help you answer these questions.
Through an engaging mix of introductions to key technologies, business insights, case examples, and your own business-focused project, your learning journey will bring into sharp focus the reality of central AI technologies today and how they can be harnessed to support your business needs.
Focusing on key AI technologies, such as machine learning, natural language processing, and robotics, the course will help you understand the implications of these new technologies for business strategy, as well as the economic and societal issues they raise. MIT expert instructors examine how artificial intelligence will complement and strengthen our workforce rather than just eliminate jobs. Additionally, the program will emphasize how the collective intelligence of people and computers together can solve business problems that not long ago were considered impossible.
You will receive a certificate of course completion at the conclusion of this course. You may also be interested in our Executive Certificates which are designed around a central themed track and consist of several courses. Learn more.
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Artificial Intelligence: Implications for Business Strategy
Artificial intelligence in medicine is the use of machine learning models to search medical data and uncover insights to help improve health outcomes and patient experiences. Thanks to recent advances in computer science and informatics, artificial intelligence (AI) is quickly becoming an integral part of modern healthcare. AI algorithms and other applications powered by AI are being used to support medical professionals in clinical settings and in ongoing research.
Currently, the most common roles for AI in medical settings are clinical decision support and imaging analysis. Clinical decision support tools help providers make decisions about treatments, medications, mental health and other patient needs by providing them with quick access to information or research that's relevant to their patient. In medical imaging, AI tools are being used to analyze CT scans, x-rays, MRIs and other images for lesions or other findings that a human radiologist might miss.
The challenges that the COVID-19 pandemic created for many health systems also led many healthcare organizations around the world to start field-testing new AI-supported technologies, such as algorithms designed to help monitor patients and AI-powered tools to screen COVID-19 patients.
The research and results of these tests are still being gathered, and the overall standards for the use AI in medicine are still being defined. Yet opportunities for AI to benefit clinicians, researchers and the patients they serve are steadily increasing. At this point, there is little doubt that AI will become a core part of the digital health systems that shape and support modern medicine.
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Artificial Intelligence in Medicine | IBM
Artificial intelligence (AI) has come to define society today in ways we never anticipated. AI makes it possible for us to unlock our smartphones with our faces, ask our virtual assistants questions and receive vocalized answers, and have our unwanted emails filtered to a spam folder without ever having to address them.
These kinds of functions have become so commonplace in our daily lives that its often easy to forget that, just a decade ago, few of them existed. Yet while artificial intelligence and machine learning may have been the topic of conversation among science fiction enthusiasts since the 80s, it wasnt until much more recently that computer scientists acquired the advanced technology and the extensive amount of data needed to create the products we use today.
The impact of machine learning and AI doesnt stop at the ability to make the lives of individuals easier, however. These programs have been developed to positively impact almost every industry through the streamlining of business processes, the improving of consumer experiences, and the carrying out of tasks that have never before been possible.
This impact of AI across industries is only expected to increase as technology continues to advance and computer scientists uncover the exciting possibilities of this specialization in their field. Below, we explore what exactly artificial intelligence entails, what careers are currently defining the industry, and how you can set yourself up for success in the AI sector.
Learn how Northeasterns advanced AI degree can accelerate your career.
The term artificial intelligence has many connotations, depending on the specific industry it is used in. Most often, however, when people say artificial intelligence, what they actually mean is machine learning, says Bethany Edmunds, associate dean and lead faculty atNortheasterns Khoury College of Computer Science. [Although AI] is a large umbrella term that incorporates a lot of statistical methods, historically, what it actually means is a computer acting like a human.
The ability of a computer to replicate human-like behavior is at the core of all AI functions. Machine learning software allows computers to witness human behavior through the intake of data. These systems then undergo advanced processes to analyze that data and identify patterns within it, using those findings to apply the discovered knowledge and replicate the behavior.
Edmunds identifies that, while advanced technology is important in this process, the key to the operation is actually the data. In fact, the astounding increase in the quantity of data collected over the last decade has had a significant impact on the advancement of the AI industry today.
Whats happening right now is that the technology has finally caught up to what people have been predicting [about AI] for a long time, she says. We finally have the right amount of data and the advanced machines that can process that data, which is why, right now, [AI] is being applied in so many sectors.
Despite the exciting opportunities that these advances are bringing to light, some individuals are still quite skeptical about the use of AI. Edmunds believes that this is due, in large part, to a lack of understanding about exactly how these processes work and the fear that comes with that.
I like to equate [the introduction of AI] to cloud computing; while people dont necessarily know how Google Drive works, they understand the concept and are faster to participate inputting their information in cloud storage, she says. AI is not like that. People dont understand the statistics behind itso it all just seems very magical.
Those who have a complex understanding of computer science and statistics, however, recognize that the potential impact of this function is endless. AI is doing amazing things today and allowing for developments across industries that weve never seen before, Edmunds says.
As the possible applications of AI continue to increase, so does the positive career potential for those with the skills needed to thrive in this industry. The World Economic Forums The Future of Jobs 2018 report predicts that there will be 58 million new jobs in artificial intelligence by 2022.
However, those with the necessary combination of skills are often hard to come by, Edmunds explains. The job market is really huge in [AI], but a lot of people arent trained for it, she says, resulting in an above-average job outlook for those who do have the skills needed to work in this niche area.
Read on to explore some of these top career areas defining the industry.
Although many of these top careers explore the application or function of AI technology, computer science and artificial intelligence research is more about discovering ways to advance the technology itself. There will always be somebody developing a faster machine, Edmunds says. Theres always going to be somebody pushing the edge, and that [person] will be a computer scientist.
Responsibilities: A computer science and artificial intelligence researchers responsibilities will vary greatly depending on their specialization or their particular role in the research field. Some may be in charge of advancing the data systems related to AI. Others might oversee the development of new software that can uncover new potential in the field. Others still may be responsible for overseeing the ethics and accountability that comes with the creation of such tools. No matter their specialization, however, individuals in these roles will work to uncover the possibilities of these technologies and then help implement changes in existing tools to reach that potential.
Career Outlook: As these individuals are at the crux of advancement in AI, their job outlook is very positive. The New York Times estimates that high-level AI researchers at top companies make more than $1,000,000 per year as of 2018, with lower-level employees making between $300,000 and $500,000 per year in both salary and stock. Individuals in base-level AI research roles are likely to make an average salary of $92,221 annually.
The AI field also relies on traditional computer science roles such as software engineers to develop the programs on which artificial intelligence tools function.
Responsibilities: Software engineers are part of the overall design and development process of digital programs or systems. In the scope of AI, individuals in these roles are responsible for developing the technical functionality of the products which utilize machine learning to carry out a variety of tasks.
Career Outlook: The Bureau of Labor Statistics predicts a growth rate of 22 percent by 2029 for software developers, including the addition of 316,000 jobs. Software engineers also make an average salary of $110,140 per year, with potential increases for those with a specialty in AI.
Many of the most popular consumer applications of AI today revolve around language. From chatbots to virtual assistants to predictive texting on smartphones, AI tools have been used to replicate human speech in a variety of formats. To do this effectively, developers call upon the knowledge of natural language processersindividuals who have both the language and technology skills needed to assist in the creation of these tools. Natural language processing is applying machine learning to language, Edmunds says. Its a really big field.
Responsibilities: As there are many applications of natural language processing, the responsibilities of the experts in this field will vary. However, in general, individuals in these roles will use their complex understanding of both language and technology to develop systems through which computers can successfully communicate with humans.
Career Outlook: Theres a real shortage of people in these roles [today], Edmunds says. There are a bunch of [products] where were trying to interact with a machine through language, but language is really hard. For this reason, those with the proper skill sets can expect an above-average salary and job outlook for the foreseeable future. The average annual salary for those with natural language processing skills is $107,641 per year.
User experience (UX) roles involve working with productsincluding those which incorporate AIto ensure that consumers understand their function and can easily use them. Although Edmunds emphasizes that these roles do exist outside of the artificial intelligence sector, the increased use of AI in technology today has led to a growing need for UX specialists that are trained in this particular area.
Responsibilities: In general, user experience specialists are in charge of understanding how humans use equipment, and thus how computer scientists can apply that understanding to the production of more advanced software. In terms of AI, a UX specialists responsibilities may include understanding how humans are interacting with these tools in order to develop functionality that better fits those humans needs down the line.
Did You Know: One of the most prominent examples of how user experience influenced technology we know today is Apple. The invention of Mac operating softwarecompared to Windowscame from the need for a product that was more user-friendly and which didnt require an advanced technical understanding to operate. Apple approached the development of the iPhone in the same way. The iPhone was all about user experience, Edmunds says. That was a [user experience expert] understanding how people interact [with their phones], including whats intuitive and whats not. Then they designed the best possible phone to fit those needs.
Job Outlook: The job outlook for user experience designers is quite positive. The average salary for UX designers is $76,440 per year (though those at the top of their field make over $100,000 annually). Job growth in this industry is expected to increase by 22.1 percent by 2022, effectively increasing opportunities for those with the right training and experience.
With data at the heart of AI and machine learning functions, those who have been trained to properly manage that data have many opportunities for success in the industry. Though data science is a broad field, Edmunds emphasizes the role that data analysts play in these AI processes as one of the most significant.
Responsibilities: Data analysts need to have a solid understanding of the data itselfincluding the practices of managing, analyzing, and storing itas well as the skills needed to effectively communicate findings through visualization. Its one thing to just have the data, but to be able to actually report on it to other people is vital, Edmunds says.
Job Outlook: Data analysts have a positive career outlook. These roles earn an average salary of $61,307 per year.
Artificial intelligence is a lucrative field with above-average job growth, but the industry remains competitive. Roles in this discipline are very niche, requiring both an advanced technical background and extensive hands-on experience. Those with this rare balance of skills and real-world exposure will be able to land any number of roles in AI and continue shaping the landscape of this constantly evolving field for years to come.
Artificial intelligence professionals share an array of practical skills and theoretical knowledge in mathematics and statistics, alongside a working understanding of role-specific tools and processes. Some AI-focused computer scientists may also pursue an understanding of the ethics and philosophy that go into giving a computer the capability to think and draw conclusions.
However, Edmunds emphasizes that, while quite advanced, these common abilities alone do not always set an individual up for a successful career in artificial intelligence. Instead, she explains, its the personal backgrounds and unique interdisciplinary skills each computer scientist brings to the table that allow them to thrive.
One of the most important factors of AI is an understanding of the application, she says. Somebody needs to look at the data [these tools use] and understand what that actually means for their specific sector.
In healthcare, for instance, an ideal AI specialist would have an understanding of data and machine learning, as well as a working knowledge of the human body. In this scenario, the specialists background in both areas allows them not only to interpret the conclusions of these AI tools, but also understand how they fit into the broader context of health.
Edmunds has also observed that, while a computer scientist with a dual background is ideal for the new kinds of applications of AI across industries, very few currently exist. If you had a dual background, you would be able to write your own check, Edmunds jokes. I can assure you, you wouldnt be looking for a job right now.
Instead of this ideal candidate, those in AI often see machine learning experts with high-level computer science and statistics abilities but without a further grasp in any particular domain. This, Edmunds identifies, is the missing piece needed for further sector-specific AI advancement.
To bridge this gap, artificial intelligence programs like those at Northeastern look to embrace students personal backgrounds or prior career paths and develop artificial intelligence specialists with the ability to make a real difference across industries.
Read More: 4 Ways Artificial Intelligence is Transforming Healthcare | AI and 3 Trends That Define the Human Resources Industry | How AI Will Transform Project Management | How Data Science is Disrupting Supply Chain Management
Those looking to either break into or advance their careers in artificial intelligence can benefit from obtaining a masters degree at a top university like Northeastern.
Those hoping to work in AI should instead consider a Master of Science in Artificial Intelligence to hone their skills, learn from top industry leaders, and obtain the real-world experience they need to properly develop a specialized career.
These practices allow Northeasterns students to prepare for their future in the changing field of artificial intelligence while always keeping the real-world aspect of their work in mind. Through experiential learning and interdisciplinary integration, [Northeasterns] masters programs are focused on developing the professional, Edmunds says. All the course work is centered around real-world problems or application domains, and we do our best to get industry practitioners in the classroom to make sure what were doing is cutting edge.
While Northeastern emphasizes the benefits of experiential learning across all of its graduate and undergraduate programs, these opportunities allow AI students specifically to practice what theyre learning in the classroom at some of the top companies in the world.
Did You Know: Northeastern has developed an array of regional campuses in locations across North America that are known for their top tech talent, including Seattle, the San Francisco Bay Area, Toronto, Charlotte, and Vancouver. These regional locations have allowed unique partnerships to develop between the university and local organizations, which happen to be among the top companies in the world. Popular co-op locations for students in these areas include Amazon, Facebook, Microsoft, Nordstrom, and Google, alongside many other leading organizations.
Northeasterns artificial intelligence program provides the rare opportunity to learn from top industry leaders, work with some of the most famous companies in the world, and develop not only relevant AI and computer science skills but those which align with your preferred specialization all before you graduate. Consider enrolling to take the first step toward a fulfilling career in the exciting artificial intelligence field.
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5 Top Careers in Artificial Intelligence
Artificial intelligence-inspired policing technology and techniques like facial recognition software and digital surveillance continue to find traction and champions among law enforcement agencies, but at what cost to the public?
Some cities like Wilmington, North Carolina, have even adopted AI-driven policing, where technology like ShotSpotter identifies gunshots and their locations. The software also recommends to patrol officers next best action based on their current location, police data on past crime records, time of day, and housing and population density.
Rene Cummings, data activist in residence at the University of Virginias School of Data Science, warns that the rules of citizenship are changing with the development of AI-inspired policing technologies. She explains, If the rules are changing, then the public needs to have a voice and has the right to provide input on where we need to go with these technologies as well as demand solutions that are accountable, explainable and ethical.
As artificial intelligence is used toward the development of technology-based solutions, Cummings research questions the ethical use of technology to collect and track citizen data, aiming to hold agencies more accountable and to provide citizens greater transparency.
Law enforcement, national security, and defense agencies are spending a lot of money on surveillance tools with little oversight as to their impact on communities and an individuals right to privacy, Cummings said. Were creating a tool that would give citizens the ability to see how these powerful tools are used and how they impact our lives.
Cummings and a team of data science graduate students are developing an algorithmic tool to evaluate the impact of AI-inspired law enforcement technologies. Their goal is to create an algorithmic force score that would eventually be used in an application that tracks technologies currently used by law enforcement agencies by force and zip code.
Sarah Adams and Claire Setser, both students in the online M.S. in Data Science program, said they chose the project because they wanted to put their data science skills to work for the public good. Cummings praised their effort. The algorithmic foundation was created with tremendous effort by Sarah and Claire who went through massive amounts of existing data to create an algorithm force model.
Adams said she wanted to work on a capstone project that contributed to and supported the ongoing efforts toward increasing police accountability and citizen activism. Our cohort chose our capstone projects at the beginning of 2021, which was less than one year after the loss of George Floyd and our country had been in civil unrest for quite some time. I was inspired by Rene Cummings energy and passion for data ethics and its application in criminology.
Setser agreed. I was attracted to this capstone project because of the possibility to enact and help push for real change. Citizens have a right to understand the technologies that are used to police them and surveil their lives every day. The problem is that this information is not readily available, so the idea of creating a tool to educate the public and encourage dialogue was of great interest to me.
Students in the M.S. in Data Science program are required to complete a capstone project sponsored by corporate, government and non-profit organizations. Students collaborate closely with sponsors and faculty across disciplines to tackle applied problems and generate data-driven solutions. Capstone projects range in scope and focus, and past projects have explored health disparities, consumer behavior, election forecasting, disease diagnosis, mental health, credit card fraud and climate change.
The capstone project was a valuable opportunity to combine and implement almost all of the skills and knowledge that we gained throughout the program, Setser said. Its an opportunity to experience the data pipeline from beginning to end while providing your sponsor a better understanding of the data. This is incredibly rewarding.
The projects next stage is to fine-tune and test, and Cummings and her team hope to collaborate with UVA and the wider Charlottesville community. What makes this so exciting is that were creating something brand new and adding new insights into emerging technology. Sarah and Claire have been amazing, delivering something extraordinary in such a short space of time. It really speaks to their expertise, determination, and commitment toward AI for the public and social good.
Cummings joined the School of Data Science in 2020 as its first data activist in residence. She is a criminologist, criminal psychologist, therapeutic jurisprudence specialist, AI ethicist and AI strategist. Her research places her on the frontline of artificial intelligence for social good, justice-oriented AI design, and social justice in AI policy and governance. She is the founder of Urban AI and a community scholar at Columbia University.
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A Closer Look at Artificial Intelligence-Inspired Policing Technologies - University of Virginia
Artificial intelligence has reached a critical turning point in its evolution, according to a new report by an international panel of experts assessing the state of the field.
Substantial advances in language processing, computer vision and pattern recognition mean that AI is touching peoples lives on a daily basis from helping people to choose a movie to aiding in medical diagnoses. With that success, however, comes a renewed urgency to understand and mitigate the risks and downsides of AI-driven systems, such as algorithmic discrimination or use of AI for deliberate deception. Computer scientists must work with experts in the social sciences and law to assure that the pitfalls of AI are minimized.
Those conclusions are from a report titled Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report, which was compiled by a panel of experts from computer science, public policy, psychology, sociology and other disciplines.AI100is an ongoing project hosted by the Stanford University Institute for Human-Centered Artificial Intelligence that aims to monitor the progress of AI and guide its future development. This new report, the second to be released by the AI100 project, assesses developments in AI between 2016 and 2021.
In the past five years, AI has made the leap from something that mostly happens in research labs or other highly controlled settings to something thats out in society affecting peoples lives, said Michael Littman, a professor of computer science at Brown University who chaired the report panel. Thats really exciting, because this technology is doing some amazing things that we could only dream about five or 10 years ago. But at the same time, the field is coming to grips with the societal impact of this technology, and I think the next frontier is thinking about ways we can get the benefits from AI while minimizing the risks.
The report is structured to answer a set of 14 questions probing critical areas of AI development. The questions were developed by the AI100 standing committee consisting of a renowned group of AI leaders. The committee then assembled a panel of 17 researchers and experts to answer them. The questions include What are the most important advances in AI? and What are the most inspiring open grand challenges? Other questions address the major risks and dangers of AI, its effects on society, its public perception and the future of the field.
While many reports have been written about the impact of AI over the past several years, the AI100 reports are unique in that they are both written by AI insiders experts who create AI algorithms or study their influence on society as their main professional activity and that they are part of an ongoing, longitudinal, century-long study, said Peter Stone, a professor of computer science at the University of Texas at Austin, executive director of Sony AI America and chair of the AI100 standing committee. The 2021 report is critical to this longitudinal aspect of AI100 in that it links closely with the 2016 report by commenting on whats changed in the intervening five years. It also provides a wonderful template for future study panels to emulate by answering a set of questions that we expect future study panels to reevaluate at five-year intervals.
Eric Horvitz, chief scientific officer at Microsoft and co-founder of the One Hundred Year Study on AI, praised the work of the study panel.
Im impressed with the insights shared by the diverse panel of AI experts on this milestone report, Horvitz said. The 2021 report does a great job of describing where AI is today and where things are going, including an assessment of the frontiers of our current understandings and guidance on key opportunities and challenges ahead on the influences of AI on people and society.
In terms of AI advances, the panel noted substantial progress across subfields of AI, including speech and language processing, computer vision and other areas. Much of this progress has been driven by advances in machine learning techniques, particularly deep learning systems, which have made the leap in recent years from the academic setting to everyday applications.
In the area of natural language processing, for example, AI-driven systems are now able to not only recognize words, but understand how theyre used grammatically and how meanings can change in different contexts. That has enabled better web search, predictive text apps, chatbots and more. Some of these systems are now capable of producing original text that is difficult to distinguish from human-produced text.
Elsewhere, AI systems are diagnosing cancers and other conditions with accuracy that rivals trained pathologists. Research techniques using AI have produced new insights into the human genome and have sped the discovery of new pharmaceuticals. And while the long-promised self-driving cars are not yet in widespread use, AI-based driver-assist systems like lane-departure warnings and adaptive cruise control are standard equipment on most new cars.
Some recent AI progress may be overlooked by observers outside the field, but actually reflect dramatic strides in the underlying AI technologies, Littman says. One relatable example is the use of background images in video conferences, which became a ubiquitous part of many peoples work-from-home lives during the COVID-19 pandemic.
To put you in front of a background image, the system has to distinguish you from the stuff behind you which is not easy to do just from an assemblage of pixels, Littman said. Being able to understand an image well enough to distinguish foreground from background is something that maybe could happen in the lab five years ago, but certainly wasnt something that could happen on everybodys computer, in real time and at high frame rates. Its a pretty striking advance.
As for the risks and dangers of AI, the panel does not envision a dystopian scenario in which super-intelligent machines take over the world. The real dangers of AI are a bit more subtle, but are no less concerning.
Some of the dangers cited in the report stem from deliberate misuse of AI deepfake images and video used to spread misinformation or harm peoples reputations, or online bots used to manipulate public discourse and opinion. Other dangers stem from an aura of neutrality and impartiality associated with AI decision-making in some corners of the public consciousness, resulting in systems being accepted as objective even though they may be the result of biased historical decisions or even blatant discrimination, the panel writes. This is a particular concern in areas like law enforcement, where crime prediction systems have been shown to adversely affect communities of color, or in health care, where embedded racial bias in insurance algorithms can affect peoples access to appropriate care.
As the use of AI increases, these kinds of problems are likely to become more widespread. The good news, Littman says, is that the field is taking these dangers seriously and actively seeking input from experts in psychology, public policy and other fields to explore ways of mitigating them. The makeup of the panel that produced the report reflects the widening perspective coming to the field, Littman says.
The panel consists of almost half social scientists and half computer science people, and I was very pleasantly surprised at how deep the knowledge about AI is among the social scientists, Littman said. We now have people who do work in a wide variety of different areas who are rightly considered AI experts. Thats a positive trend.
Moving forward, the panel concludes that governments, academia and industry will need to play expanded roles in making sure AI evolves to serve the greater good.
I was first introduced to artificial intelligence (AI) through movies and media. Movies like Star Wars made me appreciate the convenience of AI; watching C-3PO and R2D2 made me wish for a world in which a sentient robot (or rather, a protocol droid) tended to my every need. Malignant AI, as seen in 2001: Space Odyssey and The Terminator were alarming but failed to completely deteriorate my view of future AI. Media portrayals of AI showed me that I was more enticed by the convenience of AI than deterred by its potential evil.
Years later, as Ive begun to study computer science and have further considered the ethical implications of technological innovation, Ive had a change of heart. While I am beyond impressed with the rate at which AI has progressed, it is important to first investigate the potential dangers that AI can pose to society.
Unintentional (or intentional) biases
The relationship between programmers and intelligent systems mirror that of a parent and child. AIs are often given goals to achieve, but they arent given the solution to reach their goal; instead, they receive guidance and data from their programmer. The hope is that one day, the intelligent system will become autonomous and be able to perform without the guidance of its programmer. At least two problems can arise from this: (1) programmers can (unintentionally or intentionally) transfer their biases, and (2) AI can unintentionally develop biases.
One way in which AI can discriminate against groups of people is by receiving biased data from the programmer. For example, a programmer may supply AI with a dataset that might show discrimination against certain female applicants because they are likely to become pregnant soon. This data may prevent eligible applicants from being hired to the company.
On the other hand, it is possible for AI to develop biases based on the data they receive. Take the example of tech giant Amazon who used AI and machine learning to filter through resumes. Over time, they noticed that their algorithm began to favor men for their highly technical roles. This was because the training set given to the AI had a large set of male applicants. Amazon has since changed its recruiting methods, but that does not mean this problem doesnt persist.
AI also raises other ethical questions about decision-making. A perfect example is self-driving cars, which use machine learning and other sects of AI to make split-second decisions.
One ethical dilemma that self-driving cars are often faced with is the trolley problem. A common iteration of this problem is as follows: a trolley is quickly moving down tracks that lead to a fork. You can make the trolley go down alley one, where it will run over one person, or down alley two, where it will run over three people. In this situation, the AI is the decision-maker. The people who are on the tracks can represent inanimate objects, pedestrians, animals, or even the people in the car. Which life should the self-driving car value most when it is faced with an accident? This problem poses a larger question into the decision-making processes of AI.
These problems barely scratch the surface of the dangers of AI. Theres also the concept that AI can redefine the human conception. The debate about whether computers can be considered minds has been ongoing. With superintelligent AI, the possibility of their mechanisms being similar, or even indistinguishable from human minds is possible, but I digress.
While these effects are beyond scary, Im glad to see Congress paying more attention to AI regulation; in New Jersey alone, there are seven bills that have been introduced by Congress aiming to study the impacts of AI, prohibit discrimination in automated systems, and modernize state technology to use AI and other related services. While these regulations are a good start, technology has been developing faster than Congressional representatives can keep up with. Furthermore, the generational gap between representatives and those who develop and use intelligent technology is large.
We need people in Congress and other regulatory agencies who have industry and professional experience with AI to combat these issues. Intelligent system development will not sit idly by as Congress plays catch up.
Technically Speaking is an Opinion culture column used to discuss topics relating to technology, such as pop culture, trends, social media, or other relevant subject matter.
Applications Now Open for the Simplr Artificial Intelligence and Technology Scholarship – PRNewswire
SAN FRANCISCO, Sept. 16, 2021 /PRNewswire/ -- Simplr, a human-first, machine-enabled customer experience platform, today announced that they are now accepting applications for the 2022 Simplr Artificial Intelligence and Technology Scholarship. Now in its third year, the scholarship was established to support and encourage students who are pursuing an undergraduate or graduate degree in Computer Science, Mathematics, or Information Technology or are attending or will attend law school with a focus on intellectual property.
"As a successful startup involved in machine learning and AI, we like to give back by encouraging students to be creative while also earning degrees in these more difficult and technical fields," said Daniel Rodriguez, Simplr CMO. "Our hope is that winners of this scholarship become even more inspired to take a seat among the crop of leaders who will define the promise of technology for the next generation."
The $7500 scholarship will be given to the applicant who writes the most compelling essay on why they have chosen their field of study and how it applies to the development of artificial intelligence and machine learning, Blockchain technology, and the Internet of Things. Submissions will be scored by senior members of the Simplr team and other scholarship sponsors.
Applicants must be currently enrolled full time at an accredited community college, college or university, or accredited law school, or will be enrolled in the Winter of 2021 or Spring of 2022. International students and students studying under the DACA program are eligible. Entries must be submitted by December 31, 2021. The winner will be selected and notified by January 31, 2022 and will receive the funds by February 28, 2022.
For more information or to apply for the 2022 Simplr Artificial Intelligence and Technology Scholarship, click here.
Simplr offers companies a human-first, machine-enabled customer experience solution that meets the demands of the NOW Customer across all digital channels. Offering a combination of a uniquely talented, flexible, and scalable staffing pool, AI-based technology, and actionable intelligence, Simplr allows companies to immediately expand their customer service capacity and engage customers with speed, empathy, and precision. With Simplr's NOW CX solution, premium brands are eradicating customer neglect, turning browsers into buyers, and turning customers into fans. Simplr is funded byAsurion, which continues to support its growth.
Jason FidlerHead of PR and Strategic Communications[emailprotected]
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Applications Now Open for the Simplr Artificial Intelligence and Technology Scholarship - PRNewswire
Cal Poly Project Leverages Artificial Intelligence Deep Learning to Aid Wildfire Recovery – The Atascadero News
SAN LUIS OBISPO A pair of Cal Poly professors and a team of students have used artificial intelligence to train a computer to quickly assess wildfire damage potentially improving response time for efforts to recover from major wildfires.
Accurate and timely damage assessment has become critical for response and recovery as the threat of wildfires increases. Damage assessment reports inform first responders strategies, affect residents ability to file insurance claims, and guide state and federal authorities plans for future disaster relief and financial aid.
To date, most wildfire event inspectors must personally visit affected areas and manually document the severity of building damage, a process that often takes weeks.
Social sciences Assistant Professor Andrew Fricker, computer science Assistant Professor Jonathan Ventura, visiting Cal Poly undergraduate student Gustave Rousselet, and a team of Stanford doctoral students sought to streamline this process with artificial intelligence (AI) deep learning. Their project, DamageMap, involved training a computer to identify wildfire damage from aerial photos. When tested on images from Californias Camp Fire and Carr Fire, DamageMap produced results that were over 90 percent accurate as compared to official damage reports.
For Fricker, the problem had become personal.
I really wanted answers about what had happened to my childhood home in the wake of the Camp Fire (the worst in California history to date), he said, and it took weeks to find out what happened. Tens of thousands of others were in my position as well.
Meanwhile, people are living out of their cars in the Walmart parking lot, waiting for answers. If your home burned down, you could get an insurance payment for a hotel room, a vehicle, or whatever you needed to start rebuilding your life. But you cant get that insurance payout until you can prove your home has been destroyed. Hence, the need for a faster flow of information and a rapid assessment.
Fricker used funding from aCal Poly Research, Scholarly and Creative Activities (RSCA) grantto gather data, hire student research assistants, and attend Googles Geo For Good conference in 2019, when DamageMap won the Highly Inquisitive group award in a build-a-thon competition.
While previous AI technologies have compared pre-fire and post-fire aerial photos to assess damage, they have been unable to effectively meet the growing need for disaster reporting. Without deep learning, pre-fire and post-fire photo conditions must match as closely as possible to reliably analyze damage. This requires frequent surveys in fire-prone areas, making the technology difficult to implement.
However, DamageMap only requires post-fire aerial photos and could potentially scale up for wider use in wildfire damage reporting to help provide aid sooner.
If we could develop a tool to use remote sensing and artificial intelligence to shorten assessment time, theoretically we get victims faster answers and potentially get some money in the pockets of those who need it most, Fricker said.
AboutCal Polys Social Sciences Department focuses on practical training and critical thinking skills. The department offers degrees in anthropology-geography and sociology, with the opportunity to concentrate in a specific focus area. Students are taught to bridge the gap between classroom learning and the real world through internships, study abroad opportunities, service learning and senior projects.
About Cal Polys Research, Scholarly and Creative Activities Grant ProgramCal Poly is committed to the teacher-scholar model, in which faculty integrate excellence in teaching with excellence in research, scholarly and creative activities. The teacher-scholar model enables faculty to fulfill Cal Polys mission as an institution committed to quality undergraduate and graduate education, to the advancement of knowledge through basic and applied research, to the enrichment of society through creative activities in the arts and humanities, and to serving our community. The Research, Scholarly and Creative Activities (RSCA) Grant Program, supported by funds from the California State Universitys Chancellors Office and the Cal Poly Provosts Office, is intended to help faculty remain engaged in their disciplines beyond the classroom and to contribute new knowledge through robust programs of scholarship focused on strengthening California socially, culturally and economically. The annual funding from the program is intended to provide more internal resources to help faculty pursue a broader array of professional activities. Visitresearch.calpoly.edu/rsca.
When it comes to artificial intelligence, a mind is a terrible thing to waste – Federal News Network
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Tom Temin: Dr. Emery, good to have you on.
Dr. Val Emery: Hello, how are you? And thanks for having me.
Tom Temin: Tell us about this program. This is aimed at the faculty of these types of colleges, and what is it youre going to do with them?
Dr. Val Emery: The Army Faculty Immersion Program is the brainchild of the former Deputy Assistant Secretary of the Army for Research and Technology Dr. Phil Perconti. In February of 2021, the Acting Secretary of the Army hosted a HBCU technical forum announcing initiatives that were designed to get minority serving institutions more engaged with Army science and technology.
Tom Temin: This area of artificial intelligence and machine learning has become something of a strategic national issue. And it sounds like perhaps the historically Black universities and colleges and the minority serving institutions werent participating as fully as they could.
Dr. Val Emery: Absolutely. The emphasis of this, the Army has designated priority technology areas. Artificial intelligence and machine learning is one of the priority areas of emphasis. This particular topic provides an opportunity for a broad group of minority serving institutions to effectively participate, submit proposals to participate with Army scientists and engineers and major colleges and universities in a novel research project
Tom Temin: Now faculty immersion program, does that mean that faculty that are chosen go to the Army, say at the research center, and work alongside Army researchers? What does it mean to be immersed?
Dr. Val Emery: The program has three phases. The first phase, the faculty at a minority serving institution will submit a proposal detailing their interest in artificial intelligence and machine learning. But also there will be an opportunity for the university to talk about what kind of support they will be providing this faculty person when they came back to the school. Were trying to build capacity in this particular area. So by having faculty trained or developed, they will bring this new capacity or capability back to the university and expand curriculum by immersion. What the first phase will be the proposal phase, theyre selected, they will come to an Army laboratory and spend 10 weeks, a minimum of 10 weeks, in that Army laboratory working, learning the Army AI/ML infrastructure and learning the areas of emphasis for us. The second phase will be they will spend a semester at a major research institution, majority institution, working with a research faculty there to learn more about the academic side of how to develop curriculum, how to write strong proposals, how to establish a laboratory and ensure that the Army can get benefit from the research thats going on on campus. And the third phase will be with the assistance of that majority institution faculty, as well as with the Army researcher, a research project will be identified that faculty person from the minority institution will return to their home institution and conduct a fully funded research project in a topic area that they collaborate on.
Tom Temin: So in some ways, this is a three way partnership. That is with majority institutions, the Army and the minority serving institutions.
Dr. Val Emery: Absolutely. Its to expand the ecosystem, to build networks. because historically, we go to the same folk in academia, because they have the known capacity or capability. Were trying to broaden the aperture to allow more opportunity to take advantage of the human resources or human capital we have across the country.
Tom Temin: We are speaking with Dr. Val Emery, the Outreach Program Manager at the Army Combat Capabilities Development Command Research Laboratory. And in having the professors eventually go back to their home institutions to do a funded research project, is the intention that they would involve students at those institutions in the research., and thereby youre in a sense educating the educators?
Dr. Val Emery: That is absolutely the focus. We anticipate that faculty having students or teaming with other faculty to broaden and expand the capabilities at their home institutions.
Tom Temin: And what kind of professors in the first place from the minority serving institutions and the historically black colleges and universities are you looking for? Do they have to be math professors and science? Suppose someone is teaching, I dont know, the history of Flemish art, and they decide, well you know Id like to do something useful for my country and maybe work with the Army on artificial intelligence.
Dr. Val Emery: The first criteria is that all of the faculty have to be US citizens. Second criteria is they have to be whats classified as a junior faculty, in other words, not having more than seven years in teaching experience, and this way, we hope that they will grow into research and development at their institutions. With respect to the academic disciplines, if they have a capacity or capability on institutions to do AI/ML, artificial intelligence machine learning, it could be a computer scientist, a computer engineer, it can be an electrical engineer, a physicist. So a majority of the technical academic disciplines, there are potential applications for AI/ML in those departments.
Tom Temin: And what is the Army itself hoping to get?
Dr. Val Emery: The Army is hoping to get not only expanded research abroad and research in some of the areas that our events assist today. But also, theres a potential for having new talent resources coming out of these institutions who may have an interest in collaborating with us. Were building the network of academic partners. There is a potential that through some of these engagements, there can be some industry partners. So were trying to, again, expand the ecosystem of scientists and engineers that are familiar with Army science and technology. And the other piece of it is that we are trying to grow the bench from a US citizen standpoint because of national security implications.
Tom Temin: And well put a link to where people can sign up to apply but tell us what the deadlines are and what some of the timelines here are.
Dr. Val Emery: Proposals are due from the faculty members, October 1, so we have a very short timeline from this interview for folk to participate. Once we receive the applications they will be evaluated by a team of subject matter experts. And the director of basic Army director of basic research and senior team member from the Army Futures Command will do the down-select and select the finalist faculty. And we hope to have the first faculty selected by January, and in an army laboratory by the summer. Hopefully by mid May, we will have someone coming to one of our laboratories, and all of the Armys S&T infrastructure has the opportunity to participate in this program. So technical area of expertise can be very broad from the Corps of Engineers, the medical community, the Armys physical sciences community, so all of the Armys S&T infrastructure are participating in this initiative.
Tom Temin: Dr. Val Emery is the Outreach Program Manager at the Army Combat Capabilities Development Command Research Laboratory. Thanks so much for joining me.
Dr. Val Emery: Thank you for having me.
Located in the downtown area, Crowne Plaza Shanghai is adjacent to Shanghai Film Art Centre with convenient transportation. Walking along Fahua Zhen Road to the west from Jiaotong University Station No.5 exit of subway line 10 and 11, it can be arrived in only five minutes, and it has short distance from main commercial districts, such as Xujiahui, Huaihai Road, Jing'an Temple, Nanjing Road, consulate and exhibition centre. Major attractions including Xintiandi, Yu Garden, Shanghai Museum are not far away and you can also go to Shanghai Disney Resort by subway line 11 in 40 minutes.
Address:Crowne Plaza ShanghaiNo.400, Pan Yu Road, 200052 Shanghai P.R, China
Distance from major transport hubs
Hongqiao International Airport Hotel
20mins by Taxi
Pudong International Airport Hotel
50mins by Taxi
Hongqiao Railway Station Hotel
20mins by Taxi
Shanghai Railway Station Hotel
20mins by Taxi
Hongqiao International Airport Hotel
30mins by Metro
Pudong International Airport Hotel
90mins by Metro
Hongqiao Railway Station Hotel
30mins by Metro
Shanghai Railway Station Hotel
38mins by Metro
From Hongqiao International Airport
Follow Airport Avenue to Hongyu viaduct& Drive on the left front and enter Hongyu elevated road for 1.3km, Drive on the left and enter the Shanghai Chongqing Expressway for 2.9 km, Drive on the left front and enter Yan'an elevated road for 7.8km, Drive on the right front, 280 meters from Yan'an elevated road to Yan'an west road, Please go straight into Yan'an west road and drive 370 meters, Turn right into Jiangsu Road and drive 370 meters, Turn right and drive 230 meters from Huashan Road to Xingfu road, Turn right into Fahuazhen road and drive 90 meters, Turn left, arrive at the hotel from Fahuazhen road and drive 50 meters
Distance from Hotel: 15 km | Drive Time: 20 min
Typical Minimum ChargeSubway/Rail: 4.00 CNYTaxi: 40.00 CNY
From Pudong International Airport
1.5km from Shanghai Pudong International Airport to Yingbin expressway. Please go straight and enter the welcome expressway for 5.5km Drive on the right front and enter Huaxia elevated road for 16.1km. Drive on the left front and enter the Shenjiang Road overpass for 1.3km. Please go straight into the middle ring road and drive 12.3 km. Drive on the right front, 590m from the middle ring road to the North-South Elevated Road. Please go straight into the North-South Elevated Road and drive for 8.6 km. Drive on the right front, enter the overpass of Yan'an East Road, and drive 900 meters. Please go straight into Yan'an elevated road and drive for 2.8km. Drive on the right front, 250 meters from Yan'an elevated road to Yan'an west road. Please go straight into Yan'an west road and drive 790 meters
Turn left into Panyu Road and drive 810 metersTurn right into Fahuazhen road and drive for 30 meters. Turn left and drive 40 meters from Fahuazhen road to Crowne Plaza Shanghai.
Distance from Hotel: 50 km | Drive Time: 50 min
Typical Minimum ChargeSubway/Rail: 7.00 CNYTaxi: 180.00 CNY